77 research outputs found
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
In this work, we present a comparison of a shallow and a deep learning
architecture for the automated segmentation of white matter lesions in MR
images of multiple sclerosis patients. In particular, we train and test both
methods on early stage disease patients, to verify their performance in
challenging conditions, more similar to a clinical setting than what is
typically provided in multiple sclerosis segmentation challenges. Furthermore,
we evaluate a prototype naive combination of the two methods, which refines the
final segmentation. All methods were trained on 32 patients, and the evaluation
was performed on a pure test set of 73 cases. Results show low lesion-wise
false positives (30%) for the deep learning architecture, whereas the shallow
architecture yields the best Dice coefficient (63%) and volume difference
(19%). Combining both shallow and deep architectures further improves the
lesion-wise metrics (69% and 26% lesion-wise true and false positive rate,
respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho
3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes
While deep convolutional neural networks (CNN) have been successfully applied
for 2D image analysis, it is still challenging to apply them to 3D anisotropic
volumes, especially when the within-slice resolution is much higher than the
between-slice resolution and when the amount of 3D volumes is relatively small.
On one hand, direct learning of CNN with 3D convolution kernels suffers from
the lack of data and likely ends up with poor generalization; insufficient GPU
memory limits the model size or representational power. On the other hand,
applying 2D CNN with generalizable features to 2D slices ignores between-slice
information. Coupling 2D network with LSTM to further handle the between-slice
information is not optimal due to the difficulty in LSTM learning. To overcome
the above challenges, we propose a 3D Anisotropic Hybrid Network (AH-Net) that
transfers convolutional features learned from 2D images to 3D anisotropic
volumes. Such a transfer inherits the desired strong generalization capability
for within-slice information while naturally exploiting between-slice
information for more effective modelling. The focal loss is further utilized
for more effective end-to-end learning. We experiment with the proposed 3D
AH-Net on two different medical image analysis tasks, namely lesion detection
from a Digital Breast Tomosynthesis volume, and liver and liver tumor
segmentation from a Computed Tomography volume and obtain the state-of-the-art
results
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
In recent years, several convolutional neural network (CNN) methods have been
proposed for the automated white matter lesion segmentation of multiple
sclerosis (MS) patient images, due to their superior performance compared with
those of other state-of-the-art methods. However, the accuracies of CNN methods
tend to decrease significantly when evaluated on different image domains
compared with those used for training, which demonstrates the lack of
adaptability of CNNs to unseen imaging data. In this study, we analyzed the
effect of intensity domain adaptation on our recently proposed CNN-based MS
lesion segmentation method. Given a source model trained on two public MS
datasets, we investigated the transferability of the CNN model when applied to
other MRI scanners and protocols, evaluating the minimum number of annotated
images needed from the new domain and the minimum number of layers needed to
re-train to obtain comparable accuracy. Our analysis comprised MS patient data
from both a clinical center and the public ISBI2015 challenge database, which
permitted us to compare the domain adaptation capability of our model to that
of other state-of-the-art methods. For the ISBI2015 challenge, our one-shot
domain adaptation model trained using only a single image showed a performance
similar to that of other CNN methods that were fully trained using the entire
available training set, yielding a comparable human expert rater performance.
We believe that our experiments will encourage the MS community to incorporate
its use in different clinical settings with reduced amounts of annotated data.
This approach could be meaningful not only in terms of the accuracy in
delineating MS lesions but also in the related reductions in time and economic
costs derived from manual lesion labeling
Spinal cord gray matter segmentation using deep dilated convolutions
Gray matter (GM) tissue changes have been associated with a wide range of
neurological disorders and was also recently found relevant as a biomarker for
disability in amyotrophic lateral sclerosis. The ability to automatically
segment the GM is, therefore, an important task for modern studies of the
spinal cord. In this work, we devise a modern, simple and end-to-end fully
automated human spinal cord gray matter segmentation method using Deep
Learning, that works both on in vivo and ex vivo MRI acquisitions. We evaluate
our method against six independently developed methods on a GM segmentation
challenge and report state-of-the-art results in 8 out of 10 different
evaluation metrics as well as major network parameter reduction when compared
to the traditional medical imaging architectures such as U-Nets.Comment: 13 pages, 8 figure
Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation
Machine learning-based imaging diagnostics has recently reached or even
superseded the level of clinical experts in several clinical domains. However,
classification decisions of a trained machine learning system are typically
non-transparent, a major hindrance for clinical integration, error tracking or
knowledge discovery. In this study, we present a transparent deep learning
framework relying on convolutional neural networks (CNNs) and layer-wise
relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is
commonly diagnosed utilizing a combination of clinical presentation and
conventional magnetic resonance imaging (MRI), specifically the occurrence and
presentation of white matter lesions in T2-weighted images. We hypothesized
that using LRP in a naive predictive model would enable us to uncover relevant
image features that a trained CNN uses for decision-making. Since imaging
markers in MS are well-established this would enable us to validate the
respective CNN model. First, we pre-trained a CNN on MRI data from the
Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing
the CNN to discriminate between MS patients and healthy controls (n = 147).
Using LRP, we then produced a heatmap for each subject in the holdout set
depicting the voxel-wise relevance for a particular classification decision.
The resulting CNN model resulted in a balanced accuracy of 87.04% and an area
under the curve of 96.08% in a receiver operating characteristic curve. The
subsequent LRP visualization revealed that the CNN model focuses indeed on
individual lesions, but also incorporates additional information such as lesion
location, non-lesional white matter or gray matter areas such as the thalamus,
which are established conventional and advanced MRI markers in MS. We conclude
that LRP and the proposed framework have the capability to make diagnostic
decisions of..
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided
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